Posts Tagged code

In the world of data mining and analyses, there is a special set of mining methods and algorithms related to data in graphs. Graph is mathematical construct which contains vertices (nodes) and edges between them. Edge connects a pair of vertices and can be directed or undirected. As a data structure, graph is used in various extensions of this simple model. One of them is the “property graph model”, which allows vertices and edges to be uniquely identified, labeled (with one or more labels), and contain a set of key-value properties. There are a number of NoSQL graph databases which employ this data model.

Graph analyses provide measures and algorithms that help find some information which is otherwise very hard to find (i.e. using relational model or a data warehouse). Some examples: find most important people (in a social network of a kind), most important pages (on a web site or collection of sites), most important servers (in a network); decide who/what needs best protection (from flu, cyber-attacks, ..); find fake profiles and reviews on the web; cut costs (the traveling salesman problem) etc. Some of these measures are centrality measures (e.g. betweenness, closeness) or locality measures (e.g. indegree, outdegree). Also, there is a bunch of algorithms which are aimed towards identifying frequent patterns or subgraphs in a graph, or identifying interesting subgraphs or anomalies in a graph as well.

So, when I have a collection of data in my favorite relational database, how can I make use of all those graph algorithms? There are several possibilities, such as implementing the algorithms inside a relational database, but my answer to this is to perform an ETL (extract-transform-load) process in order to extract the data from the relational database and create a graph database from it. Then I can perform whatever analysis I want on this separate graph OLAP system. To do that, I defined a relational-to-graph ETL process which follows a set of 10 rules. These rules define the way the rows and their attributes (with regard to primary and foreign keys) are transformed to vertices, edges and their properties. I will not go into the details of these rules here, but if interested, please go to slides 30-40 of my IIUG2017 presentation (available here) to find explained rules with examples.

Is there any code? Of course there is :). I implemented the process as a series of stored procedures and a view, which need to be created in a relational database you want to extract the data from. After the calls:

several scripts are created in the specified directory. One script is an SQL unload-transform script, which need to be executed in the same relational database to create a bunch of data files (one per table). Other files are Cypher scripts (Cypher is an open language used by several graph database systems, like Neo4j). These scripts can be executed in a graph database of your choice in order to create and populate it with the exported data. Once the data is in the database, you can start exploring it using Cypher or some of the graph algorithms.

Title of my blog is Informix on my mind, but since I haven’t published anything for some time, one could argue that Informix was not on my mind lately :). Correctly, but only to some extend. Most of the time I’ve been busy finishing my doctorate research which I procrastinated for too long and finally defending my PhD thesis. During this time, from my perspective, only couple of things worth mentioning happened (which doesn’t mean I’m going to stop writing).

First, I gave a presentation on IIUG 2017 Conference in Raleigh, NC. In this presentation I covered some of the advantages of graph analyses and gave arguments that graph structure could be used for OLAP purposes. I also presented the algorithm and my open-source implementation of an ETL process in order to create a graph OLAP database from Informix database. Stay tuned, I’ll cover this in more detail, along with links to the code and presentation in a separate post.

Second, and of much more importance to global Informix community, was the IBM-HCL partnership deal about Informix development and maintenance. It was announced before the IIUG Conference in April, and produced a lot of different reactions, from “IBM finally killed it” to “this is a long-expected good news”. I’m not going to elaborate on the details of the deal since there is plenty of information available (let me google it for you). I was among those who were optimistic about this change, since IBM’s work on Informix was slow, especially in the last couple of years. On top of that, many great people whom worked on Informix left IBM or were moved from the product.

Now, it has been nearly half a year since HCL partnership was introduced and we’re all still waiting for any sign that this works. In this time, HCL hired some very good and Informix-caring people, for which I have high hopes that will succeed in moving things in right direction. Yet, we are still to see what happens, and I wonder how much longer should we wait.

Have you ever been asked about the history of a certain data? Like, who entered it and when? If you have, and you couldn’t figure out the answer, then you probably need some kind of auditing in place. There is a bunch of commercial (and free) products out there, most of them working with various data sources, not just Informix, some are only software solutions, other could be hardware-software combos. There’s one of IBM’s products out there aimed for this purpose, quite noticeable lately. If you know about it, then you’re probably already using it, or will use it at some point.

But what if you don’t need or can’t afford an expensive high tech solution to keep an eye on just a few tables? Or even an entire database. Well, luckily for us, Informix itself also offers audit feature, free of charge. On the second thought, maybe not.

Here’s the thing. Informix comes with auditing facility and it’s actually quite good. It covers various events it can keep track of (as much as 161 in version 12.10), some of which should be really enabled on every production system, if nothing else, for logging purposes. It also implements role separation, very important feature if you need to be confident that no one has tampered with your audit trail. It also enables you to create masks, or profiles, so you can audit different things for different types of users.

Unfortunately, most of the time, it can’t help you answer the question about the history of the data. When it comes to row level auditing, it’s mostly useless. There are four events which relate to row level auditing and could be turned on: INRW – insert row, UPRW – update row, DLRW – delete row and RDRW – read row. All things considering row level auditing on a live system should be used with utter caution, as these could produce enormous amounts of audit trails in a short time, especially the latter, as it will write one row in an audit trail file for each row read by the user. Just think of what would happen if a user would carelessly execute select statement without criteria.

At first, there was no way to select the tables which will be audited this way, so you either had to have INRW, UPRW and/or DLRW turned on for all tables or not. As this wasn’t very useful, it was enhanced soon enough. Now there’s a possibility to say which table gets to be audited this way, if some of these four events are turned on and the ADTROWS parameter is set to 1. This is done via the WITH AUDIT part of the CREATE TABLE statement:

CREATE TABLE example (a int, b int ...) WITH AUDIT;

If the table is already created, row level auditing for it could be added

ALTER TABLE example ADD AUDIT;

or removed

ALTER TABLE example DROP AUDIT;

at any time.

And now for the unfortunate part. The audit trail is written in the audit files. Every audit trail entry has the same structure, as explained here (LINK). While this format is useful or sufficient for some events, that is not the case with the row level events. For each of the row level events, only basic info is logged – tabid, partno and rowid. In the case of an update event, there are two rowids, an old and a new one. So basically, there is no way to figure out what was the value of a field before the update, or even which field changed. After the row was deleted, there is no way to find out what data it contained. The only way to know is to dig through the logical logs, as I previously explained, but that’s no way data history should be explored.

Bottom line, if you need to know your data history (inserts, updates and deletes), you should either acquire a tool which will help you with that, or you can try and set something up on your own. So here comes the happy ending. There are several way to do this, I’m showing the one with the triggers and shadow tables. The idea is to have a shadow table for each table you’re auditing, which has the same set of fields, and some extra fields, like username, operation performed on that row and a timestamp. Additionally, a set of triggers on the original table is needed, which will ensure the shadow table is filled. Informix can have multiple triggers on a single event since version 10.00 or so, so this is not a problem. If you’re thinking about implementation, there more good news – I’ve written and shared the code which will do that for you (a view and couple of procedures, find it here). All you need to do is to call the main procedure, provide the table name, dbspace name and the extent size of the shadow table, like this:

EXECUTE PROCEDURE createAuditForTable('exams, 'audit_dbs', 128);

It will create a shadow table in a designated dbspace, and name it like the original table, but with _ at the beginning. It will also create three triggers, with _ at the beginning of its name, followed by the table name and the operation name at the end, so it could be identified more easily (in this example, shadow table will be _exams and triggers will be named _examsinsert, _examsupdate, _examsdelete). These triggers will fire on each insert, update or delete operation on the original table and write actual row data, operation performed, user who performed it (USER variable) and the time of the operation (CURRENT variable) in the shadow table. Actual row is considered to be new row (in the for each row trigger) in the case of insert or update operation, and the old row in the case of a delete operation.

So from this moment on, you just have to query the shadow table in order to get the history of the original table. If the delete occurs, there is a whole row stored in the shadow table that can be seen. If the update occurred, you can find the previous row with the same primary key to compare the differences. Once you decide that no more audit is necessary on a table, simply drop the shadow table and these triggers.

Obviously, there is a storage space required for this, but so is for every other type of auditing, except this one is managed inside the database. It’s easy to govern and explore it, only SQL interface is needed.

There are security issues at place here, since no real role separation can be implemented. Triggers could be disabled or dropped, shadow tables with audit trail could be tampered with. Some problems could be solved by eliminating users with elevated permissions, like resource or DBA, and part of it could be solved by storing or moving the shadow tables to another server. But still, database system administrator (DBSA, i.e. informix user) can override all of this. So if you need to be in compliance with some kind of regulation, this type of auditing is not for you.

Otherwise, if you need to know your data history from an end-user point of view, this simple auditing can help you. All the code needed could be found here, it’s all open sourced via the GNU GPL v3 license. Feel free to use it and extend it. After noting that I cannot be held responsible if anything goes wrong :), just put it in your database, end execute the procedure with the correct arguments – tables you want audited.